CN111679288B - Method for measuring spatial distribution of point cloud data - Google Patents
Method for measuring spatial distribution of point cloud data Download PDFInfo
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- CN111679288B CN111679288B CN202010567544.1A CN202010567544A CN111679288B CN 111679288 B CN111679288 B CN 111679288B CN 202010567544 A CN202010567544 A CN 202010567544A CN 111679288 B CN111679288 B CN 111679288B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4808—Evaluating distance, position or velocity data
Abstract
The invention provides a method for measuring the spatial distribution of point cloud data, which is used for solving the problem that the spatial distribution of the point cloud data in the prior art cannot be quantized. According to the point cloud data spatial distribution measuring method, the spatial distribution of point cloud data is quantitatively described through measuring factors, the measuring factors comprise effective lattice rate, point cloud distribution uniformity and redundancy, the effective lattice rate reflects the coverage condition of the point cloud data, and the point cloud distribution uniformity and redundancy respectively reflect the uniformity and redundancy of the point cloud distribution. The point cloud data spatial distribution measuring method quantitatively describes the quality of point cloud data, the high-quality point cloud data has high effective lattice rate, high point cloud distribution uniformity and low point cloud distribution redundancy, the low-quality point cloud data has low effective lattice rate, low point cloud distribution uniformity and high point cloud distribution redundancy, and the data acquisition mode is restricted by the point cloud spatial distribution measuring factor, so that the laser radar and photogrammetric data acquisition work can be effectively guided.
Description
Technical Field
The invention belongs to the field of computer information processing, and particularly relates to a method for measuring spatial distribution of point cloud data.
Background
Point cloud data is a collection of points with three-dimensional spatial information obtained by scanning or computer vision, the scanning signal being recorded in the form of points, each point containing three-dimensional coordinates, some possibly color information (RGB) or Intensity information (Intensity). And point cloud data of the earth surface object can be obtained through laser radar and photogrammetry.
The spatial distribution of the laser radar point cloud data is influenced by factors such as scanning modes and flight attitudes. For the equiangular velocity scanning mode, the distance between the echo points of the laser pulse is gradually increased along with the increase of the scanning angle; for the oscillation scanning mode, the distance between the echo points in the nadir direction is larger than the distance between the echo points at the edge of the scanning line; the higher the fly height, the greater the distance between echo points within a scanline. The scanning frequency and the flying speed can influence the interval between the scanning lines, the shaking of the attitude of the aircraft can also influence the interval between the scanning lines, and the phenomenon of alternate density is easy to occur. The photogrammetry adopts a computer vision algorithm to reconstruct point cloud data from the high-overlapping-rate images, and the spatial distribution of the reconstructed point cloud is influenced by factors such as the image overlapping rate, the illumination condition and the like. The higher the image overlapping rate is, the denser point cloud data can be obtained, and the more uniform reconstructed point cloud data can be obtained under ideal illumination conditions. If the solar altitude is too low, the radiation dynamic range of the image is narrow, and for a forest region, reconstruction of a forest canopy region is prone to failure, and a lot of holes appear. The spatial distribution of the point cloud data will directly affect the observation accuracy of the forest structure and the estimation accuracy of the relevant single tree and forest stand factors. Due to the insufficiency of the directly obtained point cloud data, the point cloud data needs to be processed in order to obtain the spatial distribution information represented by the point cloud data.
In the prior art, a method for measuring spatial distribution of point cloud data is to calculate a point cloud density in a certain area and reflect the spatial distribution of the point cloud data through the point cloud density. However, this method cannot quantitatively describe the spatial distribution characteristics of the point cloud data, and cannot effectively guide the data acquisition of the laser radar and the photogrammetry.
Disclosure of Invention
In order to quantitatively describe the spatial distribution of point cloud data and effectively guide data acquisition of laser radar and photogrammetry, the embodiment of the invention provides a method for measuring the spatial distribution of the point cloud data.
In order to achieve the above purpose, the technical solution adopted by the embodiment of the present invention is as follows:
a method for measuring the spatial distribution of point cloud data comprises the following steps:
step S1, determining a grid range according to a point cloud data coverage area, and rasterizing the point cloud data in the grid range according to a preset spatial resolution;
s2, counting the number of effective grid units and the number of all grid units, calculating an effective lattice rate according to the number of the effective grid units and the number of all grid units, and quantifying the coverage state of the point cloud data through the effective lattice rate;
s3, calculating grid point density according to the number of points of the point cloud data and the number of all grid units;
s4, calculating point cloud distribution uniformity according to the effective lattice rate and the lattice point density, and quantizing the point cloud data uniformity through the point cloud distribution uniformity;
and S5, calculating point cloud distribution redundancy according to the effective lattice rate and the lattice point density, and quantifying the point cloud data redundancy through the point cloud distribution redundancy.
As a preferred embodiment of the present invention, the effective lattice rate is calculated in step S2, and the effective lattice rate R is calculated by equation (1):
wherein N is E The number of effective grid units is N is the number of all grid units, the value range of the effective grid rate R is more than 0Real numbers less than or equal to 1.
As a preferred embodiment of the present invention, in the step S3, the lattice point density D is calculated according to equation (2) C ：
Wherein N is the number of points in the point cloud data, N is the number of all grid units, and the grid point density D C Real numbers with a value range greater than 0.
As a preferred embodiment of the present invention, in the step S4, a point cloud distribution uniformity PCH is calculated by equation (3):
wherein D is C The lattice point density, R is the effective lattice rate, and the uniformity PCH span is a real number greater than 0 and less than or equal to 1.
As a preferred embodiment of the present invention, in the step S5, a point cloud distribution redundancy PCR is calculated by equation (4):
wherein D is C And the redundancy PCR value range is a real number which is greater than or equal to 0 and less than 1.
The invention has the following beneficial effects:
according to the method for measuring the spatial distribution of the point cloud data, the spatial distribution of the laser radar point cloud data and the photogrammetric reconstruction dense point cloud data is quantitatively described through the measurement factors, the measurement factors comprise effective lattice rate, point cloud distribution uniformity and redundancy, the effective lattice rate reflects the coverage condition of the point cloud data, the point cloud distribution uniformity reflects the uniformity condition of the point cloud distribution, and the point cloud distribution redundancy reflects the redundancy condition of the point cloud data. According to the point cloud data spatial distribution measuring method, the quality of the point cloud data is described quantitatively, the high-quality point cloud data has high effective lattice rate, high point cloud distribution uniformity and low point cloud distribution redundancy, the low-quality point cloud data has low effective lattice rate, low point cloud distribution uniformity and high point cloud distribution redundancy, and the data acquisition mode is constrained through the point cloud spatial distribution measuring factors, so that the acquisition work of laser radars and photogrammetric data can be guided effectively.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for measuring spatial distribution of point cloud data according to an embodiment of the present invention.
Detailed Description
The technical problems, aspects and advantages of the present invention will be explained in detail below with reference to exemplary embodiments. The exemplary embodiments described below are only for illustrating the present invention and should not be construed as limiting the present invention. It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For a certain spatial scale, the coverage range of the point cloud data comprises a data area and a cavity area, and the proportion of the data area, namely the effective lattice rate, needs to be judged; for a certain effective lattice rate, the number of points in the grid unit is different, and the uniformity and redundancy of point cloud data distribution need to be judged. The effective lattice rate, the uniformity and the redundancy of the point cloud data distribution can help determine relevant parameters during data acquisition. The embodiment of the invention provides a method for measuring the spatial distribution of point cloud data, which is used for effectively measuring the spatial distribution of the point cloud data by providing measurement factors comprising effective lattice rate, point cloud distribution uniformity and redundancy.
Fig. 1 is a schematic flow chart illustrating a method for measuring spatial distribution of point cloud data according to an embodiment of the present invention. As shown in fig. 1, the method for measuring spatial distribution of point cloud data includes the following steps:
step S1, determining a grid range according to a point cloud data coverage area, and rasterizing the point cloud data in the grid range according to a preset spatial resolution.
The point cloud data comprises spatial position information, and the point cloud data is rasterized according to a certain spatial resolution to obtain a grid unit, wherein the grid unit value is the number of the point clouds in a grid unit range.
Further, the rasterizing in this step specifically includes:
s11, according to the space range of the point cloud data coverage area, taking the upper left-corner geographical coordinate of the space range as the upper left-corner geographical coordinate of the grid data;
step S12, calculating the number of columns of the grid units according to the east-west direction width of the point cloud data coverage area and the size of the grid units;
s13, calculating the number of lines of the grid unit according to the length of the point cloud data in the north-south direction and the size of the grid unit;
step S14; calculating row and column coordinates of the grid units corresponding to the discrete points according to the plane geographic coordinates of each discrete point in the point cloud data;
step S15, the grid cell value is equal to the number of discrete points located within the grid cell. If no discrete point is located within the grid cell, then the grid cell value is 0; if there are 1 discrete points located within the grid cell, then the grid cell value is 1; if there are 2 discrete points located within the grid cell, the grid cell value is 2, and so on. The method does not depend on the number N of points of the point cloud data and does not depend on the number N of all grid units. S2, counting the number of effective grid units and the number of all grid units, calculating an effective lattice rate according to the number of the effective grid units and the number of all grid units, and quantifying the coverage state of the point cloud data through the effective lattice rate.
In this step, the number of the effective grid units N E : and counting the grid units in the grid range, and if the grid unit value is greater than 0, adding 1 to the number of the effective grid units. Number of all grid cells N: and counting the grid cells in the grid range, wherein the grid cells comprise the number of effective grid cells and the number of ineffective grid cells.
Specifically, in the step of calculating the effective rate, the effective rate R is calculated by the formula (1):
wherein N is E The number of the effective grid units is N, the number of all the grid units is N, and the value range of the effective lattice rate R is a real number which is larger than 0 and smaller than or equal to 1.
If the effective lattice rate R is equal to 1, points exist in each grid unit; if the effective lattice rate is larger, for example, 0.7, it means that there are fewer holes in the coverage area of the point cloud; if the effective lattice rate is smaller, for example 0.2, it indicates that there are more holes in the coverage area of the point cloud, and the phenomenon of data loss is obvious.
And S3, calculating the grid point density according to the number of the points of the point cloud data and the number of all grid units.
In this step, the lattice point density D is calculated according to the formula (2) C ：
Wherein N is the number of points in the point cloud data, N is the number of all grid units, and the grid point density D C Real numbers with a value range greater than 0.
If the lattice point densityD C Less than 1, which means theoretically under-sampling, some grid units do not have sampling points; if the lattice point density D C Greater than 1, which means theoretical oversampling, some grid cells will have multiple sampling points; if the lattice point density D C Equal to 1, indicating an average of one point in each grid cell.
And S4, calculating the point cloud distribution uniformity according to the effective lattice rate and the lattice point density, and quantifying the point cloud data uniformity through the point cloud distribution uniformity.
In this step, the point cloud distribution uniformity PCH is calculated by the formula (3):
wherein D is C For lattice density, R is the effective lattice rate, and the uniformity PCH span is a real number greater than 0 and less than or equal to 1.
If the lattice point density is equal to 1, indicating that the uniformity is equal to the effective lattice rate; if the lattice point density is greater than 1, for the same effective lattice rate, the larger the lattice point density is, the smaller the uniformity is, and the more uneven the distribution of the representing points is; if the lattice point density is less than 1, the influence of undersampled grid units needs to be eliminated when calculating the uniformity, and the effective lattice rate is corrected (R/D) according to the lattice point density C ) And calculating the distribution uniformity of the point cloud according to an exponential relation, wherein when the corrected effective lattice rate is equal to 1, the uniformity is always equal to 1, and when the lattice point density is fixed (less than 1), the corrected effective lattice rate is smaller and the uniformity is smaller.
And S5, calculating point cloud distribution redundancy according to the effective lattice rate and the lattice point density, and quantifying the point cloud data redundancy through the point cloud distribution redundancy.
In the step, point cloud distribution redundancy PCR is calculated by formula (4):
wherein D is C The lattice point density, R the effective lattice rate and the redundancy PCR valueReal numbers ranging from 0 to 1 are included.
If the effective lattice rate is equal to the lattice point density (namely the effective lattice number is equal to the number of points), no redundant point is shown, namely the redundancy is equal to 0; if the effective lattice rate is smaller than the lattice point density, indicating that redundant points exist, when the effective lattice rate is fixed, the larger the lattice point density is, the larger the redundancy is, and when the lattice point density is fixed, the smaller the effective lattice rate is, the larger the redundancy is; when the effective lattice rate is much smaller than the lattice point density, the redundancy is close to 1.
According to the technical scheme, the method for measuring the spatial distribution of the point cloud data quantitatively describes the spatial distribution of the laser radar point cloud data and the photogrammetric reconstruction dense point cloud data, the measurement factors comprise the effective lattice rate, the point cloud distribution uniformity and the redundancy rate, the effective lattice rate reflects the coverage condition of the point cloud data, the point cloud distribution uniformity reflects the uniform condition of the point cloud distribution, and the point cloud distribution redundancy reflects the redundancy condition of the point cloud data. The measurement factors in the embodiment of the invention can be used for quantitatively describing the quality of the point cloud data, the high-quality point cloud data has high effective lattice rate, high point cloud distribution uniformity and low point cloud distribution redundancy, and the low-quality point cloud data has low effective lattice rate, low point cloud distribution uniformity and high point cloud distribution redundancy. The technical scheme can be used for guiding the data acquisition work of laser radar and photogrammetry, and the data acquisition mode is restricted by the point cloud space distribution measurement factors.
While the foregoing is directed to the preferred embodiment of the present invention, it is understood that the invention is not limited to the exemplary embodiments disclosed, but is made merely for the purpose of providing those skilled in the relevant art with a comprehensive understanding of the specific details of the invention. It will be apparent to those skilled in the art that various modifications and adaptations of the present invention can be made without departing from the principles of the invention and the scope of the invention is to be determined by the claims.
Claims (3)
1. A method for measuring the spatial distribution of point cloud data is characterized by comprising the following steps:
step S1, determining a grid range according to a point cloud data coverage area, and rasterizing the point cloud data in the grid range according to a preset spatial resolution;
s2, counting the number of effective grid units and the number of all grid units, calculating an effective lattice rate according to the number of the effective grid units and the number of all grid units, and quantifying the coverage state of the point cloud data through the effective lattice rate;
s3, calculating grid point density according to the number of points of the point cloud data and the number of all grid units;
s4, calculating the point cloud distribution uniformity according to the effective lattice rate and the lattice point density, quantizing the point cloud data uniformity through the point cloud distribution uniformity, and calculating the point cloud distribution uniformity PCH through the formula (3):
wherein D is C The lattice point density is shown, R is the effective lattice rate, and the value range of the uniformity PCH is real number which is larger than 0 and smaller than or equal to 1;
specifically, if the lattice point density is equal to 1, it indicates uniformity equal to the effective lattice rate; if the lattice point density is greater than 1, for the same effective lattice rate, the larger the lattice point density is, the smaller the uniformity is, and the more uneven the distribution of the representing points is; if the lattice point density is less than 1, the influence of undersampling grid units is required to be eliminated when the uniformity is calculated, the effective lattice rate is corrected according to the lattice point density, then the point cloud distribution uniformity is calculated according to an exponential relationship, when the corrected effective lattice rate is equal to 1, the uniformity is always equal to 1, and when the lattice point density is fixed, the smaller the corrected effective lattice rate is, the smaller the uniformity is;
step S5, calculating point cloud distribution redundancy according to the effective lattice rate and the lattice point density, quantizing the point cloud data redundancy through the point cloud distribution redundancy, and calculating point cloud distribution redundancy PCR through the formula (4):
wherein D is C The lattice point density is shown, R is the effective lattice rate, and the redundancy PCR value range is a real number which is more than or equal to 0 and less than 1;
specifically, if the effective lattice rate is equal to the lattice point density, it means that there are no redundant points, i.e., the redundancy is equal to 0; if the effective lattice rate is smaller than the lattice point density, indicating that redundant points exist, when the effective lattice rate is fixed, the larger the lattice point density is, the larger the redundancy is, and when the lattice point density is fixed, the smaller the effective lattice rate is, the larger the redundancy is; when the effective lattice rate is much smaller than the lattice point density, the redundancy is close to 1.
2. The method of measuring spatial distribution of point cloud data according to claim 1, wherein the effective lattice rate is calculated in step S2, and the effective lattice rate R is calculated by equation (1):
wherein, N E The number of the effective grid units is N, the number of all the grid units is N, and the value range of the effective lattice rate R is a real number which is larger than 0 and smaller than or equal to 1.
3. The method for measuring spatial distribution of point cloud data according to claim 1, wherein in the step S3, the lattice point density D is calculated according to formula (2) C ：
Wherein N is the number of points in the point cloud data, N is the number of all grid units, and the grid point density D C Real numbers with a value range greater than 0.
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| CN113340266A (en) * | 2021-06-02 | 2021-09-03 | 江苏豪杰测绘科技有限公司 | Indoor space surveying and mapping system and method |
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